Best local AI models for AMD Pro W6600X

The largest models that fit fully

The 30 largest of the 123 models that fit; every smaller model in the catalog fits too. Best quant means the highest quality compression whose weights and 4k context both sit inside the memory.

Close, but only with CPU offload

These need more than the card holds at their smallest practical quant, so part of the model runs from system memory (figures assume 32 GB of it). They work, several times slower.

How to read this

The AMD Radeon Pro W6600X is equipped with 8 GB of GDDR6 memory. This dedicated graphics memory determines the size of the artificial intelligence models you can run locally. To run a model entirely on your graphics hardware, the model files and the active memory space must fit within this 8 GB limit. Keeping the model inside the graphics memory ensures fast processing speeds.

The quantization column indicates the compression level used on each model. Raw models are often too large for local hardware, so developers use quantization to reduce their size. For example, a Q4_K_M quant uses a four bit quantization method, while a Q6_K quant uses a six bit method. Higher quantization numbers like Q6_K preserve more original model accuracy but require more memory. A lower quant like Q4_K_M allows larger models to fit into your available space.

Several high quality models fit completely within the 8 GB limit of your hardware. You can run the Mochi 1 10B model at a Q4_K_M quant which uses 7.3 GB of memory. The Gemma 2 9B model fits at a Q4_K_M quant using exactly 8 GB. You can also run Nemotron Nano 9B, GLM-4 9B, Yi-Coder 9B, GLM-4-9B-Chat, and GLM-4V-9B at a Q5_K_M quant using 7.7 GB of memory. Chroma 8.9B fits at Q5_K_M using 7.6 GB, and Llama 3.1 8B fits at Q5_K_M using 7.4 GB.

Other models can run at the higher Q6_K quantization level. Granite 3.3 8B, Ministral 8B, InternLM 3 8B, OpenCoder 8B, Seed-Coder 8B, MiniCPM-V 2.6, Idefics 3 8B, Fuyu-8B, Emu3, and Stable Diffusion 3.5 Large all fit at Q6_K using 7.9 GB of memory. EXAONE 3.5 7.8B fits at Q6_K using 7.7 GB. Mistral 7B fits at Q6_K using 7.4 GB. Popular 7B models like Qwen2.5 7B, OLMo 2 7B, Falcon 3 7B, Command R7B, OpenHermes 2.5, Zephyr 7B Beta, OpenChat 3.5, Starling LM 7B, and Codestral Mamba 7B all fit at Q6_K using 6.9 GB.

If a model exceeds your 8 GB graphics memory, you can offload the extra data to your system RAM. This offload process allows you to run larger models but reduces processing speed. For instance, FLUX.1 dev 12B needs 14.4 GB at FP8 and requires 16.4 GB of system RAM. Vicuna 13B needs 9.5 GB at Q4_K_M and requires 11.5 GB of system RAM. Gemma 3 12B, Gemma 4 12B, Mistral NeMo 12B, Pixtral 12B, FLUX.1 schnell 12B, FLUX.1 Kontext dev 12B, and FLUX.1 Krea dev 12B all need 8.8 GB at Q4_K_M and require 10.8 GB of system RAM. Open-Sora 2.0 11B needs 8.1 GB at Q4_K_M and requires 10.1 GB of system RAM.

When planning your local setup, remember that these memory figures are calculated using a standard 4k context window. The context window is the amount of text the model can process at one time. If you increase the context window beyond 4k tokens, the model will require significantly more memory. This extra memory usage can push a model over your 8 GB limit and force the system to use slower system RAM.